So what exactly are the key factors driving machine learning’s growth in the business world? Below, we outline some of this technology’s best applications

Dynamic Pricing

Also known as “demand pricing,” dynamic pricing involves analyzing clusters of data to be able to put a price tag on products and services that maximizes revenue. It works by filtering all possible data to determine the cost—such as the level of consumer-interest, demand at the time of purchase, the market’s reaction to an ongoing campaign, and more.

With this principle, the machine-run Churn algorithm divides the number of order cancellations by the number of active customers within a set period. It’s a very simple format, but one with a lot of insights. It helps businesses identify which of their customers are likely to stop buying their products and why, thus helping them come up with solutions to improve customer retention rates.

Customer Segmentation

Customer segmentation is the process of dividing your customers into groups based on common characteristics. Much like dynamic pricing, this learning algorithm also analyzes multiple sets of data to be able to segregate customers.

For instance, businesses might divide them according to age, gender, profession, location, purchasing history, and more. This helps companies come up with multiple marketing strategies that can target the different groups in their consumer base simultaneously.

Malware Detection

Machine learning and cybersecurity go hand-in-hand. After all, most (if not all) security systems are powered by machine learning programs.

HP’s guide to the most effective antivirus options outlines how even the most fundamental of security tools rely on complex algorithms to detect hidden threats. Every executable file that attempts to enter your systems goes through a scanner. If it matches any code in the antivirus’ updating malware database, it then bars it from entering.

Similar cybersecurity essentials like anti-spyware systems, behavioral blockers, and VPNs are run by machine learning algorithms, too. No business is exempt from cyber attacks and data theft, so cybersecurity is indeed an important part of every operation.

Recruiting Automation

Machine learning has a lot of uses in recruitment, from talent sourcing to candidate assessment.

For instance, Canadian-based tech startup Ideal created a system to complement existing HR software. It works by connecting to third-party recruitment websites and automatically searches for potential candidates based on the given parameters, thus helping you make more informed talent decisions.

The same technology also screens résumés to quickly identify a position’s desirable skills through set-pattern recognition. Other machine learning recruitment tools like Sourcehub and Textio have similar functions.

Chatbots

Chatbots are a one-time, big-time investment that will save businesses 30% of their customer service expenditures, according to Chatbots Life.

By integrating a chatbot into your website, you can open customer service inquiries 24/7. Many of the customer’s questions are simple enough that your bot can be programmed to answer them. But for special cases, the machine can also transfer the concern to a live agent.

This way, employees will spend less time replying to questions, and focus more on providing meaningful solutions to complex problems.

Visual Search

Visual search is an emerging technology that’s revolutionizing how people find and buy products.

Big eCommerce brands like Alibaba and eBay, as well as online retailers like Nordstrom and Neiman Marcus, are already using this technology.

Recommendation Engine

A recommendation engine is one of the most widespread applications of machine learning in the business world. It’s a system that suggests products and services to individual users based on analysing their data.

A very popular use of this feature is Netflix and Amazon recommendations. Based on a range of data—such as users’ search queries and purchase or watch history—the machine can suggest similar movies and products. Incidentally, chatbots can also be programmed to do the recommending, so that the list of products get delivered straight to consumers’ inboxes.

Machine learning has a lot of applications, be it data analytics or product recommendations. As such, no matter what business you have, it’s important to understand that new advancements like machine learning are important to keep business moving forward.